



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					[[] for _ in range(a__	)]
			lowercase__       	=					key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"	)
			if key == 1 or len(a__	) <= key:
						return input_string

			for position, character in enumerate(a__	):
						lowercase__       	=					position % (lowest * 2)  # puts it in bounds
						lowercase__       	=					min(a__    ,	lowest * 2 - num	)  # creates zigzag pattern
						temp_grid[num].append(a__	)
			lowercase__       	=					["".join(a__	) for row in temp_grid]
			lowercase__       	=					"".join(a__	)

			return output_string



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					[]
			lowercase__       	=					key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"	)
			if key == 1:
						return input_string

			lowercase__       	=					[[] for _ in range(a__	)]  # generates template
			for position in range(len(a__	)	):
						lowercase__       	=					position % (lowest * 2)  # puts it in bounds
						lowercase__       	=					min(a__    ,	lowest * 2 - num	)  # creates zigzag pattern
						temp_grid[num].append("*"	)

			lowercase__       	=					0
			for row in temp_grid:  # fills in the characters
						lowercase__       	=					input_string[counter : counter + len(a__	)]
						grid.append(list(a__	)	)
						counter += len(a__	)

			lowercase__       	=					""  # reads as zigzag
			for position in range(len(a__	)	):
						lowercase__       	=					position % (lowest * 2)  # puts it in bounds
						lowercase__       	=					min(a__    ,	lowest * 2 - num	)  # creates zigzag pattern
						output_string += grid[num][0]
						grid[num].pop(0	)
			return output_string



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					{}
			for key_guess in range(1    ,	len(a__	)	):  # tries every key
						lowercase__       	=					decrypt(a__    ,	a__	)
			return results


if __name__ == "__main__":
							import doctest

							doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''LongformerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LongformerForMaskedLM''',
       '''LongformerForMultipleChoice''',
       '''LongformerForQuestionAnswering''',
       '''LongformerForSequenceClassification''',
       '''LongformerForTokenClassification''',
       '''LongformerModel''',
       '''LongformerPreTrainedModel''',
       '''LongformerSelfAttention''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLongformerForMaskedLM''',
       '''TFLongformerForMultipleChoice''',
       '''TFLongformerForQuestionAnswering''',
       '''TFLongformerForSequenceClassification''',
       '''TFLongformerForTokenClassification''',
       '''TFLongformerModel''',
       '''TFLongformerPreTrainedModel''',
       '''TFLongformerSelfAttention''',
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




